Projet ML2: US elections¶

Auteur: Sylvie Jarjayes¶

Cadre: DU Data Analytics de la Sorbonne¶

Dans ce notebook, il s'agit de prédire les résultats des élections américaines de 2020 à partir de données démographiques et sociologiques.¶

1.Données des élections 2020:¶

import, création target, premières stats, création features: état + longitude et latitude¶

1.1 Import Données et création target¶

In [240]:
#!pip install mlxtend
In [241]:
#!pip install xgboost
In [243]:
#!pip install dtreeviz
In [122]:
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px


from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, RobustScaler, MaxAbsScaler
from sklearn.preprocessing import PolynomialFeatures

from sklearn.preprocessing import PowerTransformer

from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector

from sklearn.svm import SVC
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.model_selection import GridSearchCV


from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet

from sklearn.metrics import f1_score, recall_score, precision_score, make_scorer

from mlxtend.plotting import plot_decision_regions

from sklearn.metrics import f1_score, make_scorer

from sklearn.ensemble import RandomForestClassifier


from sklearn.model_selection import (
    train_test_split, 
    GridSearchCV,
    cross_val_predict
)

import xgboost as xgb
from sklearn import metrics

import sklearn as sk

from sklearn.ensemble import RandomForestClassifier

from sklearn.tree import export_graphviz

from sklearn import tree
from dtreeviz.trees import dtreeviz 

from sklearn.ensemble import StackingClassifier
In [2]:
# Propriétés des graphiques

from matplotlib import style
#print(plt.style.available)

plt.style.use('seaborn')
In [3]:
# Propriétés d'affichage
pd.options.display.max_columns = None
pd.options.display.max_rows = None
In [3]:
# Vérification des fichiers présents dans le répertoire courant
os.listdir()
Out[3]:
['.ipynb_checkpoints',
 '.~lock.Education.xls#',
 '.~lock.PopulationEstimates.xls#',
 '.~lock.PovertyEstimates.xls#',
 '.~lock.state_code.csv#',
 '05_support_vector_machines.ipynb.txt',
 '2020_US_County_Level_Presidential_Results.csv',
 'county_statistics.csv',
 'Education.xls',
 'PopulationEstimates.xls',
 'PovertyEstimates.xls',
 'projet_ml2_us_elections.ipynb',
 'projet_ml2_us_elections_15102022.ipynb',
 'projet_ml2_us_elections_16102022.ipynb',
 'projet_ml2_us_elections_v2.ipynb',
 'projet_ml2_us_elections_v3.ipynb',
 'SDA - Projet Classification.pdf',
 'state_code.csv',
 'Untitled.ipynb',
 'us_county_latlng.csv']
In [4]:
# Import des données des élections 2020
data = pd.read_csv('2020_US_County_Level_Presidential_Results.csv')
# Visu
data.head()
Out[4]:
state_name county_fips county_name votes_gop votes_dem total_votes diff per_gop per_dem per_point_diff
0 Alabama 1001 Autauga County 19838 7503 27770 12335 0.714368 0.270184 0.444184
1 Alabama 1003 Baldwin County 83544 24578 109679 58966 0.761714 0.224090 0.537623
2 Alabama 1005 Barbour County 5622 4816 10518 806 0.534512 0.457882 0.076631
3 Alabama 1007 Bibb County 7525 1986 9595 5539 0.784263 0.206983 0.577280
4 Alabama 1009 Blount County 24711 2640 27588 22071 0.895716 0.095694 0.800022
In [5]:
# Création de la target: =1 si votes rep > votes dem sino =0
data['target']=0
data['target'].loc[data['diff']>0]=1
data.drop(columns=['diff','per_point_diff'], inplace=True)
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3152 entries, 0 to 3151
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   state_name   3152 non-null   object 
 1   county_fips  3152 non-null   int64  
 2   county_name  3152 non-null   object 
 3   votes_gop    3152 non-null   int64  
 4   votes_dem    3152 non-null   int64  
 5   total_votes  3152 non-null   int64  
 6   per_gop      3152 non-null   float64
 7   per_dem      3152 non-null   float64
 8   target       3152 non-null   int64  
dtypes: float64(2), int64(5), object(2)
memory usage: 221.8+ KB

C:\Users\sylvi\AppData\Local\Temp\ipykernel_9812\2223062755.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data['target'].loc[data['diff']>0]=1
In [8]:
print(f"data comporte {data.shape[0]} lignes et {data.shape[1]} colonnes.")

data comporte 3152 lignes et 9 colonnes.

1.2 Bilan des votes par état¶

In [7]:
# Import d'un fichier contenant les codes des états
state_code = pd.read_csv('state_code.csv')
state_code.head()

# Création d'une colonne avec les codes des états: première colonne de data
state_code = state_code.rename(columns={'State':'state_name'} )
state_code = state_code.rename(columns={'Alpha code':'state_code'} )
data = pd.merge(data, state_code, how="left", on=["state_name"])
first_column = data.pop('state_code')
data.insert(0, 'state_code', first_column)
data.head()
Out[7]:
state_code state_name county_fips county_name votes_gop votes_dem total_votes per_gop per_dem target
0 AL Alabama 1001 Autauga County 19838 7503 27770 0.714368 0.270184 1
1 AL Alabama 1003 Baldwin County 83544 24578 109679 0.761714 0.224090 1
2 AL Alabama 1005 Barbour County 5622 4816 10518 0.534512 0.457882 1
3 AL Alabama 1007 Bibb County 7525 1986 9595 0.784263 0.206983 1
4 AL Alabama 1009 Blount County 24711 2640 27588 0.895716 0.095694 1
In [8]:
# Création de data_by_state: bilan des votes par états
data_by_state = data.groupby('state_code')[['votes_gop','votes_dem']].sum().sort_values(by='state_code')
data_by_state['pc_dem']=data_by_state['votes_dem']/(data_by_state['votes_gop']+data_by_state['votes_dem'])
data_by_state['pc_gop'] = 1-data_by_state['pc_dem']
data_by_state.head()
Out[8]:
votes_gop votes_dem pc_dem pc_gop
state_code
AK 189892 153405 0.446858 0.553142
AL 1441168 849648 0.370893 0.629107
AR 760647 423932 0.357876 0.642124
AZ 1661686 1672143 0.501568 0.498432
CA 6005961 11109764 0.649097 0.350903
In [9]:
# Carte des US avec le % de votes par état
fig = px.choropleth(data_by_state,
                   locations=data_by_state.index,
                   locationmode='USA-states',
                   scope='usa',
                   color = 'pc_gop',
                   hover_data = ['pc_gop', 'pc_dem'],
                   color_continuous_scale = 'viridis_r',
                   range_color = [0.1,0.9])

fig.add_scattergeo(
    locations=data_by_state.index,
    hoverinfo='skip',
    locationmode='USA-states',
    text=data_by_state.index,
    mode='text')

fig.update_layout(
    title={'text':'Vote républicain en 2020',
           'xanchor':'center',
           'yanchor':'top',
           'x':0.5})


fig.show()
In [10]:
# Bilan par état: votes dem vs gop
color_1 = plt.cm.Reds(np.linspace(0.6, 1, 66))
color_2 = plt.cm.Blues(np.linspace(0.6, 1, 66))
sns.set(font_scale=1.5)

def data_by_state_bar(data1, data2):
    x = np.arange(51)
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(30,10))
    w = 0.3
    i = 0
    color = color_1
    plt.title('Nombre de votes par partis politiques en 2020: Bleu=Dem, Rouge=Rep')
    plt.xticks(x + w /2, data_by_state.index, rotation='90')
    ax.set_xlabel('Etats')
    ax.set_ylabel('Nombre de votes en 2020')
    ax.bar(x,data_by_state[data1].values,color=color_1,width=w,align='center')

    color = color_2
    ax1 = ax
    ax1.bar(x + w,data_by_state[data2].values, color=color_2,width=w,align='center')
In [11]:
data_by_state_bar('votes_gop', 'votes_dem')

1.3 Import latitude et longitude pour visualisation¶

In [12]:
# Import data localisation: fips, long, lat
data_loc = pd.read_csv('us_county_latlng.csv')

# Classement par fips croissant puis visualisation des premières lignes de data_loc:
data_loc.sort_values(by=['fips_code']).head()
Out[12]:
fips_code name lng lat
2527 1001 Autauga -86.642900 32.535142
145 1003 Baldwin -87.722745 30.727825
2528 1005 Barbour -85.391068 31.870090
2058 1007 Bibb -87.126814 32.998376
3052 1009 Blount -86.567006 33.980871
In [13]:
# Il y a plus de fips code que dans les data des élections
data_loc.drop(columns=['name'], inplace=True)

# On renomme la colonne des fips avant de merger: création de data_geo: data+long+lat
data_loc = data_loc.rename(columns={'fips_code':'county_fips'} )
data_geo = pd.merge(data, data_loc, how="left", on=["county_fips"])
data_geo.head()
Out[13]:
state_code state_name county_fips county_name votes_gop votes_dem total_votes per_gop per_dem target lng lat
0 AL Alabama 1001 Autauga County 19838 7503 27770 0.714368 0.270184 1 -86.642900 32.535142
1 AL Alabama 1003 Baldwin County 83544 24578 109679 0.761714 0.224090 1 -87.722745 30.727825
2 AL Alabama 1005 Barbour County 5622 4816 10518 0.534512 0.457882 1 -85.391068 31.870090
3 AL Alabama 1007 Bibb County 7525 1986 9595 0.784263 0.206983 1 -87.126814 32.998376
4 AL Alabama 1009 Blount County 24711 2640 27588 0.895716 0.095694 1 -86.567006 33.980871
In [14]:
# Scatter plot des votes dem en utilisant la longitute et la latitude
plt.figure(figsize = (14,10))
sns.scatterplot(data = data_geo, x = "lng", y = "lat", hue = "per_gop", palette = "coolwarm", size='total_votes')
plt.title("Elections de 2020: pourcentage de votes républicains")
Out[14]:
Text(0.5, 1.0, 'Elections de 2020: pourcentage de votes républicains')

1.4 Import Education: analyse des variables influentes¶

In [15]:
education = pd.read_excel('Education.xls', skiprows = 4)
education.head()
Out[15]:
FIPS Code State Area name 2003 Rural-urban Continuum Code 2003 Urban Influence Code 2013 Rural-urban Continuum Code 2013 Urban Influence Code Less than a high school diploma, 1970 High school diploma only, 1970 Some college (1-3 years), 1970 Four years of college or higher, 1970 Percent of adults with less than a high school diploma, 1970 Percent of adults with a high school diploma only, 1970 Percent of adults completing some college (1-3 years), 1970 Percent of adults completing four years of college or higher, 1970 Less than a high school diploma, 1980 High school diploma only, 1980 Some college (1-3 years), 1980 Four years of college or higher, 1980 Percent of adults with less than a high school diploma, 1980 Percent of adults with a high school diploma only, 1980 Percent of adults completing some college (1-3 years), 1980 Percent of adults completing four years of college or higher, 1980 Less than a high school diploma, 1990 High school diploma only, 1990 Some college or associate's degree, 1990 Bachelor's degree or higher, 1990 Percent of adults with less than a high school diploma, 1990 Percent of adults with a high school diploma only, 1990 Percent of adults completing some college or associate's degree, 1990 Percent of adults with a bachelor's degree or higher, 1990 Less than a high school diploma, 2000 High school diploma only, 2000 Some college or associate's degree, 2000 Bachelor's degree or higher, 2000 Percent of adults with less than a high school diploma, 2000 Percent of adults with a high school diploma only, 2000 Percent of adults completing some college or associate's degree, 2000 Percent of adults with a bachelor's degree or higher, 2000 Less than a high school diploma, 2015-19 High school diploma only, 2015-19 Some college or associate's degree, 2015-19 Bachelor's degree or higher, 2015-19 Percent of adults with less than a high school diploma, 2015-19 Percent of adults with a high school diploma only, 2015-19 Percent of adults completing some college or associate's degree, 2015-19 Percent of adults with a bachelor's degree or higher, 2015-19
0 0 US United States NaN NaN NaN NaN 52373312.0 34158051.0 11650730.0 11717266.0 47.7 31.1 10.6 10.7 44535197.0 45947035.0 20794975.0 21558480.0 33.5 34.6 15.7 16.2 39343718.0 47642763.0 39571702.0 32310253.0 24.8 30.0 24.9 20.3 35715625.0 52168981.0 49864428.0 44462605.0 19.6 28.6 27.4 24.4 26472261.0 59472748.0 63756905.0 70920162.0 11.998918 26.956844 28.898697 32.145542
1 1000 AL Alabama NaN NaN NaN NaN 1062306.0 468269.0 136287.0 141936.0 58.7 25.9 7.5 7.8 964840.0 704207.0 278205.0 270063.0 43.5 31.8 12.5 12.2 843638.0 749591.0 553512.0 399228.0 33.1 29.4 21.7 15.7 714081.0 877216.0 746495.0 549608.0 24.7 30.4 25.9 19.0 458922.0 1022839.0 993344.0 845772.0 13.819302 30.800268 29.912098 25.468332
2 1001 AL Autauga County 2.0 2.0 2.0 2.0 6611.0 3757.0 933.0 767.0 54.8 31.1 7.7 6.4 7074.0 6145.0 2104.0 2117.0 40.6 35.2 12.1 12.1 6252.0 6671.0 4912.0 3026.0 30.0 32.0 23.5 14.5 5872.0 9332.0 7413.0 4972.0 21.3 33.8 26.9 18.0 4291.0 12551.0 10596.0 9929.0 11.483395 33.588459 28.356571 26.571573
3 1003 AL Baldwin County 4.0 5.0 3.0 2.0 18726.0 8426.0 2334.0 2038.0 59.4 26.7 7.4 6.5 18125.0 15380.0 6602.0 5498.0 39.7 33.7 14.5 12.1 17309.0 20544.0 15900.0 10870.0 26.8 31.8 24.6 16.8 17258.0 28428.0 28178.0 22146.0 18.0 29.6 29.3 23.1 13893.0 41797.0 47274.0 48148.0 9.193843 27.659616 31.284081 31.862459
4 1005 AL Barbour County 6.0 6.0 6.0 6.0 8120.0 2242.0 581.0 861.0 68.8 19.0 4.9 7.3 7759.0 3671.0 1362.0 1300.0 55.1 26.1 9.7 9.2 6965.0 4258.0 2622.0 1857.0 44.4 27.1 16.7 11.8 6679.0 6124.0 4025.0 2068.0 35.3 32.4 21.3 10.9 4812.0 6396.0 4676.0 2080.0 26.786907 35.604542 26.029837 11.578713
In [23]:
education.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3283 entries, 0 to 3282
Data columns (total 47 columns):
 #   Column                                                                    Non-Null Count  Dtype  
---  ------                                                                    --------------  -----  
 0   FIPS Code                                                                 3283 non-null   int64  
 1   State                                                                     3283 non-null   object 
 2   Area name                                                                 3283 non-null   object 
 3   2003 Rural-urban Continuum Code                                           3221 non-null   float64
 4   2003 Urban Influence Code                                                 3221 non-null   float64
 5   2013 Rural-urban Continuum Code                                           3221 non-null   float64
 6   2013 Urban Influence Code                                                 3221 non-null   float64
 7   Less than a high school diploma, 1970                                     3186 non-null   float64
 8   High school diploma only, 1970                                            3186 non-null   float64
 9   Some college (1-3 years), 1970                                            3186 non-null   float64
 10  Four years of college or higher, 1970                                     3186 non-null   float64
 11  Percent of adults with less than a high school diploma, 1970              3186 non-null   float64
 12  Percent of adults with a high school diploma only, 1970                   3186 non-null   float64
 13  Percent of adults completing some college (1-3 years), 1970               3186 non-null   float64
 14  Percent of adults completing four years of college or higher, 1970        3186 non-null   float64
 15  Less than a high school diploma, 1980                                     3267 non-null   float64
 16  High school diploma only, 1980                                            3267 non-null   float64
 17  Some college (1-3 years), 1980                                            3267 non-null   float64
 18  Four years of college or higher, 1980                                     3267 non-null   float64
 19  Percent of adults with less than a high school diploma, 1980              3267 non-null   float64
 20  Percent of adults with a high school diploma only, 1980                   3267 non-null   float64
 21  Percent of adults completing some college (1-3 years), 1980               3267 non-null   float64
 22  Percent of adults completing four years of college or higher, 1980        3267 non-null   float64
 23  Less than a high school diploma, 1990                                     3271 non-null   float64
 24  High school diploma only, 1990                                            3271 non-null   float64
 25  Some college or associate's degree, 1990                                  3271 non-null   float64
 26  Bachelor's degree or higher, 1990                                         3271 non-null   float64
 27  Percent of adults with less than a high school diploma, 1990              3271 non-null   float64
 28  Percent of adults with a high school diploma only, 1990                   3271 non-null   float64
 29  Percent of adults completing some college or associate's degree, 1990     3270 non-null   float64
 30  Percent of adults with a bachelor's degree or higher, 1990                3271 non-null   float64
 31  Less than a high school diploma, 2000                                     3272 non-null   float64
 32  High school diploma only, 2000                                            3272 non-null   float64
 33  Some college or associate's degree, 2000                                  3272 non-null   float64
 34  Bachelor's degree or higher, 2000                                         3272 non-null   float64
 35  Percent of adults with less than a high school diploma, 2000              3272 non-null   float64
 36  Percent of adults with a high school diploma only, 2000                   3272 non-null   float64
 37  Percent of adults completing some college or associate's degree, 2000     3272 non-null   float64
 38  Percent of adults with a bachelor's degree or higher, 2000                3272 non-null   float64
 39  Less than a high school diploma, 2015-19                                  3273 non-null   float64
 40  High school diploma only, 2015-19                                         3273 non-null   float64
 41  Some college or associate's degree, 2015-19                               3273 non-null   float64
 42  Bachelor's degree or higher, 2015-19                                      3273 non-null   float64
 43  Percent of adults with less than a high school diploma, 2015-19           3273 non-null   float64
 44  Percent of adults with a high school diploma only, 2015-19                3273 non-null   float64
 45  Percent of adults completing some college or associate's degree, 2015-19  3273 non-null   float64
 46  Percent of adults with a bachelor's degree or higher, 2015-19             3273 non-null   float64
dtypes: float64(44), int64(1), object(2)
memory usage: 1.2+ MB
In [16]:
# Suppression de colonnes inutiles

education.drop(columns=['Area name', '2003 Rural-urban Continuum Code',
                       '2003 Urban Influence Code','2013 Rural-urban Continuum Code' ,
                       '2013 Urban Influence Code'], inplace=True)


# On renomme les colonnes des pourcentages en fonction des diplômes depuis 1990
list_niveau= ['Percent of adults with less than a high school diploma, ',
             'Percent of adults with a high school diploma only, ',
              "Percent of adults completing some college or associate's degree, ",
             "Percent of adults with a bachelor's degree or higher, "]
short_niveau=["pc_no_diploma", "pc_high_school", "pc_degree", "pc_high_degree"]


list_year = ['1990', '2000', '2015-19']
short_year = ['90', '00', '15-19']

def rename_education(old_name, new_name):

    for i,x in enumerate(list_year) :
        abrev = short_year[i]
        old = old_name+x
        new = new_name+'_'+abrev
        education.rename(columns={old: new}, inplace=True)
        
for i,x in enumerate(list_niveau):
    old_name=x
    new_name=short_niveau[i]
    rename_education(old_name, new_name)
In [17]:
# Réduction du nombre de variables
col_educ= education.columns.tolist()
col_sel=['FIPS Code','pc_no_diploma_90', 'pc_high_school_90','pc_degree_90','pc_high_degree_90',
'pc_no_diploma_00','pc_high_school_00','pc_degree_00','pc_high_degree_00',  'pc_no_diploma_15-19',
'pc_high_school_15-19', 'pc_degree_15-19','pc_high_degree_15-19']  

# Création d'un dataframe education_short après sélection des colonnes de col_sel
education_short=education[col_sel]
education_short.head()
Out[17]:
FIPS Code pc_no_diploma_90 pc_high_school_90 pc_degree_90 pc_high_degree_90 pc_no_diploma_00 pc_high_school_00 pc_degree_00 pc_high_degree_00 pc_no_diploma_15-19 pc_high_school_15-19 pc_degree_15-19 pc_high_degree_15-19
0 0 24.8 30.0 24.9 20.3 19.6 28.6 27.4 24.4 11.998918 26.956844 28.898697 32.145542
1 1000 33.1 29.4 21.7 15.7 24.7 30.4 25.9 19.0 13.819302 30.800268 29.912098 25.468332
2 1001 30.0 32.0 23.5 14.5 21.3 33.8 26.9 18.0 11.483395 33.588459 28.356571 26.571573
3 1003 26.8 31.8 24.6 16.8 18.0 29.6 29.3 23.1 9.193843 27.659616 31.284081 31.862459
4 1005 44.4 27.1 16.7 11.8 35.3 32.4 21.3 10.9 26.786907 35.604542 26.029837 11.578713
In [40]:
#print(f"education comporte {education.shape[0]} lignes et  {education.shape[1]} colonnes.")
In [18]:
# Fusion data et population: il faut d'abord renommer la colonne fips de population
education_short.rename(columns={'FIPS Code':'county_fips'}, inplace=True)
# Merge des 2 dataframe: on ne conserve que les codes fips présents dans data
data_educ = pd.merge(data_geo, education_short, how="left", on=["county_fips"])

C:\Users\sylvi\AppData\Local\Temp\ipykernel_9812\2665277104.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [26]:
#print(f"data_educ comporte {data_educ.shape[0]} lignes et  {data_educ.shape[1]} colonnes.")
In [27]:
#data_educ.head()
In [19]:
# Création d'un dataframe avec données groupées par état
data_educ_state = data_educ.groupby(by=['state_name']).median()
data_educ_state.head()

# Fonction graphique barplot
def data_educ_bar():
    x = np.arange(51)
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(30,10))
    w = 0.3
    i = 0

    plt.title('Pourcentage moyen par état des non diplômés en 2015-2019' )
    plt.xticks(x + w /2, data_educ_state.index, rotation='90')
    ax.set_xlabel('Etat')
    ax.set_ylabel('Pourcentage moyen par état des non diplômés en 2015-2019')
    
    sns.barplot( x=data_educ_state.index, y='pc_no_diploma_15-19', hue='target', data=data_educ_state)

# Affichage
sns.set(font_scale=1.5)
data_educ_bar()
In [20]:
# Analyse graphique de l'influence du niveau scolaire sur le vote
fig, ax = plt.subplots(figsize=(20,6))
sns.violinplot(x='target', y='pc_no_diploma_15-19', data=data_educ, ax=ax)
fig, ax = plt.subplots(figsize=(20,6))
sns.violinplot(x='target', y='pc_high_school_15-19', data=data_educ, ax=ax)
fig, ax = plt.subplots(figsize=(20,6))
sns.violinplot(x='target', y='pc_degree_15-19', data=data_educ, ax=ax)
fig, ax = plt.subplots(figsize=(20,6))
sns.violinplot(x='target', y='pc_high_degree_15-19', data=data_educ, ax=ax)
Out[20]:
<AxesSubplot:xlabel='target', ylabel='pc_high_degree_15-19'>
In [21]:
# Affichage des résultats sur la carte des US
plt.figure(figsize = (14,10))
sns.scatterplot(data = data_educ, x = "lng", y = "lat", 
                hue = "per_gop", palette = "coolwarm_r", size='pc_high_degree_15-19')
plt.title("Elections 2020: pourcentage de votes républicains")
Out[21]:
Text(0.5, 1.0, 'Elections 2020: pourcentage de votes républicains')
In [155]:
#data_educ.drop(columns=['county_fips','votes_gop','votes_dem']).corr()['target']

1.5 Import poverty: analyse des variables influentes¶

In [33]:
#os.listdir()
In [22]:
poverty = pd.read_excel('PovertyEstimates.xls', skiprows = 4)
poverty.head()
Out[22]:
FIPStxt Stabr Area_name Rural-urban_Continuum_Code_2003 Urban_Influence_Code_2003 Rural-urban_Continuum_Code_2013 Urban_Influence_Code_2013 POVALL_2019 CI90LBALL_2019 CI90UBALL_2019 PCTPOVALL_2019 CI90LBALLP_2019 CI90UBALLP_2019 POV017_2019 CI90LB017_2019 CI90UB017_2019 PCTPOV017_2019 CI90LB017P_2019 CI90UB017P_2019 POV517_2019 CI90LB517_2019 CI90UB517_2019 PCTPOV517_2019 CI90LB517P_2019 CI90UB517P_2019 MEDHHINC_2019 CI90LBINC_2019 CI90UBINC_2019 POV04_2019 CI90LB04_2019 CI90UB04_2019 PCTPOV04_2019 CI90LB04P_2019 CI90UB04P_2019
0 0 US United States NaN NaN NaN NaN 39490096 39248096 39732096 12.3 12.2 12.4 12000470 11865995 12134945 16.8 16.6 17.0 8258906 8160650 8357162 15.8 15.6 16.0 65712 65594 65830 3457689.0 3405854.0 3509524.0 18.2 17.9 18.5
1 1000 AL Alabama NaN NaN NaN NaN 747478 730491 764465 15.6 15.2 16.0 233890 225712 242068 21.9 21.1 22.7 161670 154564 168776 20.7 19.8 21.6 51771 51179 52363 69236.0 65296.0 73176.0 24.2 22.8 25.6
2 1001 AL Autauga County 2.0 2.0 2.0 2.0 6723 5517 7929 12.1 9.9 14.3 2040 1472 2608 15.9 11.5 20.3 1376 902 1850 14.4 9.4 19.4 58233 52517 63949 NaN NaN NaN NaN NaN NaN
3 1003 AL Baldwin County 4.0 5.0 3.0 2.0 22360 18541 26179 10.1 8.4 11.8 6323 4521 8125 13.5 9.6 17.4 4641 3295 5987 13.3 9.4 17.2 59871 54593 65149 NaN NaN NaN NaN NaN NaN
4 1005 AL Barbour County 6.0 6.0 6.0 6.0 5909 4787 7031 27.1 22.0 32.2 2050 1560 2540 41.0 31.2 50.8 1468 1114 1822 39.5 30.0 49.0 35972 31822 40122 NaN NaN NaN NaN NaN NaN
In [23]:
# Suppression des variables inutiles
poverty.rename(columns={'FIPStxt':'county_fips'}, inplace=True)
poverty.drop(columns=['Rural-urban_Continuum_Code_2003','Urban_Influence_Code_2003',
                     'Rural-urban_Continuum_Code_2013','Urban_Influence_Code_2013',
                     'Stabr','Area_name'], inplace=True)

# Sélection des données les plus récentes
poverty_short=poverty[['county_fips','PCTPOVALL_2019','PCTPOV017_2019', 'PCTPOV517_2019']]

# Création du dataframe mis à jour
data_educ_pov = pd.merge(data_educ, poverty_short, how="left", on=["county_fips"])
data_educ_pov.head()
Out[23]:
state_code state_name county_fips county_name votes_gop votes_dem total_votes per_gop per_dem target lng lat pc_no_diploma_90 pc_high_school_90 pc_degree_90 pc_high_degree_90 pc_no_diploma_00 pc_high_school_00 pc_degree_00 pc_high_degree_00 pc_no_diploma_15-19 pc_high_school_15-19 pc_degree_15-19 pc_high_degree_15-19 PCTPOVALL_2019 PCTPOV017_2019 PCTPOV517_2019
0 AL Alabama 1001 Autauga County 19838 7503 27770 0.714368 0.270184 1 -86.642900 32.535142 30.0 32.0 23.5 14.5 21.3 33.8 26.9 18.0 11.483395 33.588459 28.356571 26.571573 12.1 15.9 14.4
1 AL Alabama 1003 Baldwin County 83544 24578 109679 0.761714 0.224090 1 -87.722745 30.727825 26.8 31.8 24.6 16.8 18.0 29.6 29.3 23.1 9.193843 27.659616 31.284081 31.862459 10.1 13.5 13.3
2 AL Alabama 1005 Barbour County 5622 4816 10518 0.534512 0.457882 1 -85.391068 31.870090 44.4 27.1 16.7 11.8 35.3 32.4 21.3 10.9 26.786907 35.604542 26.029837 11.578713 27.1 41.0 39.5
3 AL Alabama 1007 Bibb County 7525 1986 9595 0.784263 0.206983 1 -87.126814 32.998376 48.2 33.8 13.2 4.7 36.8 35.7 20.4 7.1 20.942602 44.878773 23.800098 10.378526 20.3 25.9 25.2
4 AL Alabama 1009 Blount County 24711 2640 27588 0.895716 0.095694 1 -86.567006 33.980871 39.5 34.7 18.8 7.0 29.6 36.0 24.8 9.6 19.509438 33.422131 33.975021 13.093413 16.3 21.0 21.4
In [24]:
# Influence de la pauvreté en 2019 sur le vote républicain: visu carte US
plt.figure(figsize = (14,10))
sns.scatterplot(data = data_educ_pov, x = "lng", y = "lat", 
                hue = "per_gop", palette = "coolwarm_r", size='PCTPOVALL_2019')
plt.title("Influence de la pauvreté en 2019 sur le vote républicain")
Out[24]:
Text(0.5, 1.0, 'Influence de la pauvreté en 2019 sur le vote républicain')
In [25]:
# Influence de la pauvreté en 2019 sur le type de vote
fig, ax = plt.subplots(figsize=(15,4))
sns.violinplot(x='target', y='PCTPOVALL_2019', inner='box', linewidth=5, data=data_educ_pov, ax=ax)
fig, ax = plt.subplots(figsize=(20,6))
sns.violinplot(x='target', y='PCTPOV017_2019', linewidth=5, data=data_educ_pov, ax=ax)
fig, ax = plt.subplots(figsize=(20,6))
sns.violinplot(x='target', y='PCTPOV517_2019', linewidth=5, data=data_educ_pov, ax=ax)
Out[25]:
<AxesSubplot:xlabel='target', ylabel='PCTPOV517_2019'>

1.6 Import population: merge avec data_educ_pov = data_complet¶

In [27]:
# Import données démographiques
population = pd.read_excel('PopulationEstimates.xls', skiprows = 2)
#population.columns
In [28]:
# Suppression colonnes inutiles
population.rename(columns={'FIPStxt':'county_fips'}, inplace=True)
population.drop(columns=['Area_Name', 'Rural-urban_Continuum Code_2003',
                         'Rural-urban_Continuum Code_2013', 'Urban_Influence_Code_2003',
                         'Urban_Influence_Code_2013', 'Economic_typology_2015',
                         'CENSUS_2010_POP', 'ESTIMATES_BASE_2010'], inplace=True)

population=population.iloc[1:]
list_pop = population.columns.to_list()
col_drop_pop = list_pop[1:103]

population.drop(columns=col_drop_pop, inplace=True)

# Création du dataframe final
data_complet = pd.merge(data_educ_pov, population, how="left", on=["county_fips"])

# Visualisation du dataframe final: data_complet
data_complet.head()
Out[28]:
state_code state_name county_fips county_name votes_gop votes_dem total_votes per_gop per_dem target lng lat pc_no_diploma_90 pc_high_school_90 pc_degree_90 pc_high_degree_90 pc_no_diploma_00 pc_high_school_00 pc_degree_00 pc_high_degree_00 pc_no_diploma_15-19 pc_high_school_15-19 pc_degree_15-19 pc_high_degree_15-19 PCTPOVALL_2019 PCTPOV017_2019 PCTPOV517_2019 R_birth_2011 R_birth_2012 R_birth_2013 R_birth_2014 R_birth_2015 R_birth_2016 R_birth_2017 R_birth_2018 R_birth_2019 R_death_2011 R_death_2012 R_death_2013 R_death_2014 R_death_2015 R_death_2016 R_death_2017 R_death_2018 R_death_2019 R_NATURAL_INC_2011 R_NATURAL_INC_2012 R_NATURAL_INC_2013 R_NATURAL_INC_2014 R_NATURAL_INC_2015 R_NATURAL_INC_2016 R_NATURAL_INC_2017 R_NATURAL_INC_2018 R_NATURAL_INC_2019 R_INTERNATIONAL_MIG_2011 R_INTERNATIONAL_MIG_2012 R_INTERNATIONAL_MIG_2013 R_INTERNATIONAL_MIG_2014 R_INTERNATIONAL_MIG_2015 R_INTERNATIONAL_MIG_2016 R_INTERNATIONAL_MIG_2017 R_INTERNATIONAL_MIG_2018 R_INTERNATIONAL_MIG_2019 R_DOMESTIC_MIG_2011 R_DOMESTIC_MIG_2012 R_DOMESTIC_MIG_2013 R_DOMESTIC_MIG_2014 R_DOMESTIC_MIG_2015 R_DOMESTIC_MIG_2016 R_DOMESTIC_MIG_2017 R_DOMESTIC_MIG_2018 R_DOMESTIC_MIG_2019 R_NET_MIG_2011 R_NET_MIG_2012 R_NET_MIG_2013 R_NET_MIG_2014 R_NET_MIG_2015 R_NET_MIG_2016 R_NET_MIG_2017 R_NET_MIG_2018 R_NET_MIG_2019
0 AL Alabama 1001 Autauga County 19838 7503 27770 0.714368 0.270184 1 -86.642900 32.535142 30.0 32.0 23.5 14.5 21.3 33.8 26.9 18.0 11.483395 33.588459 28.356571 26.571573 12.1 15.9 14.4 11.600000 11.163449 10.412013 11.676701 11.862569 12.097324 12.220585 11.377262 11.202671 9.345455 10.165092 10.612595 10.454297 10.641690 9.935790 10.358573 9.339812 9.712572 2.254545 0.998357 -0.200582 1.222405 1.220879 2.161534 1.862012 2.037449 1.490099 0.072727 -0.254127 0.218816 0.127714 0.236887 -0.054492 -0.216933 -0.126214 -0.287248 5.945455 -5.971992 -4.121042 1.842729 -1.949762 4.831664 1.066590 0.667129 4.847310 6.018182 -6.226119 -3.902226 1.970443 -1.712875 4.777171 0.849656 0.540916 4.560062
1 AL Alabama 1003 Baldwin County 83544 24578 109679 0.761714 0.224090 1 -87.722745 30.727825 26.8 31.8 24.6 16.8 18.0 29.6 29.3 23.1 9.193843 27.659616 31.284081 31.862459 10.1 13.5 13.3 11.842995 11.112202 11.219905 11.226489 11.225449 11.204755 10.949200 10.734799 10.446871 9.895312 10.007884 9.869361 10.084554 10.434644 9.850441 9.992336 10.744094 10.546624 1.947683 1.104318 1.350544 1.141935 0.790805 1.354314 0.956865 -0.009294 -0.099753 0.957611 1.268904 1.059658 0.573505 0.651544 0.876894 0.409405 0.450769 0.362739 15.684259 16.219674 21.691816 19.610829 17.074420 20.402397 21.988851 24.276447 24.017829 16.641870 17.488579 22.751474 20.184334 17.725964 21.279291 22.398256 24.727215 24.380567
2 AL Alabama 1005 Barbour County 5622 4816 10518 0.534512 0.457882 1 -85.391068 31.870090 44.4 27.1 16.7 11.8 35.3 32.4 21.3 10.9 26.786907 35.604542 26.029837 11.578713 27.1 41.0 39.5 12.109461 11.007155 10.423983 9.833867 10.219088 10.597247 10.988364 10.513902 10.331329 11.816785 10.493487 10.904521 11.472845 12.519326 10.750830 11.577026 13.152372 12.591307 0.292676 0.513667 -0.480538 -1.638978 -2.300238 -0.153583 -0.588662 -2.638470 -2.259978 -0.182922 -0.440286 -0.369645 0.148998 0.490215 0.652729 0.470930 0.479722 0.524638 0.475598 -6.457531 -7.762540 -5.289429 -16.214789 -18.890745 -25.469458 -9.234644 -5.690302 0.292676 -6.897817 -8.132185 -5.140431 -15.724575 -18.238016 -24.998528 -8.754922 -5.165664
3 AL Alabama 1007 Bibb County 7525 1986 9595 0.784263 0.206983 1 -87.126814 32.998376 48.2 33.8 13.2 4.7 36.8 35.7 20.4 7.1 20.942602 44.878773 23.800098 10.378526 20.3 25.9 25.2 11.575140 10.834141 11.418961 11.225984 11.126133 12.225372 12.894364 10.330165 10.723621 12.101282 10.393729 12.171373 10.959755 11.746714 10.675053 11.166253 11.710488 11.259802 -0.526143 0.440412 -0.752412 0.266229 -0.620581 1.550319 1.728111 -1.380324 -0.536181 0.438452 0.836783 0.885191 0.621201 0.576254 0.620128 0.443105 0.445266 0.446818 -5.436808 -4.624328 -6.683190 0.709944 0.753563 -1.328845 -3.677774 -7.302358 1.385134 -4.998356 -3.787545 -5.797999 1.331144 1.329817 -0.708717 -3.234669 -6.857092 1.831952
4 AL Alabama 1009 Blount County 24711 2640 27588 0.895716 0.095694 1 -86.567006 33.980871 39.5 34.7 18.8 7.0 29.6 36.0 24.8 9.6 19.509438 33.422131 33.975021 13.093413 16.3 21.0 21.4 12.946335 12.367553 11.232736 10.751661 12.446546 12.171796 11.450282 11.751674 11.263268 9.901162 10.196283 10.121616 10.195840 11.003720 11.302382 12.508566 11.907440 11.367077 3.045173 2.171270 1.111121 0.555821 1.442826 0.869414 -1.058284 -0.155766 -0.103809 -0.278416 0.086851 0.781257 0.694776 0.225985 0.382542 -0.017349 0.086537 0.103809 0.487228 -1.737016 -1.128482 -2.744366 -1.564510 -1.773605 6.210911 -0.155766 1.020788 0.208812 -1.650165 -0.347225 -2.049590 -1.338525 -1.391062 6.193562 -0.069229 1.124597
In [29]:
# Nettoyage final
data_complet.drop(columns=['votes_gop', 'votes_dem','lng','lat'], inplace=True)

# Statistiques
data_complet.describe()
Out[29]:
county_fips total_votes per_gop per_dem target pc_no_diploma_90 pc_high_school_90 pc_degree_90 pc_high_degree_90 pc_no_diploma_00 pc_high_school_00 pc_degree_00 pc_high_degree_00 pc_no_diploma_15-19 pc_high_school_15-19 pc_degree_15-19 pc_high_degree_15-19 PCTPOVALL_2019 PCTPOV017_2019 PCTPOV517_2019 R_birth_2011 R_birth_2012 R_birth_2013 R_birth_2014 R_birth_2015 R_birth_2016 R_birth_2017 R_birth_2018 R_birth_2019 R_death_2011 R_death_2012 R_death_2013 R_death_2014 R_death_2015 R_death_2016 R_death_2017 R_death_2018 R_death_2019 R_NATURAL_INC_2011 R_NATURAL_INC_2012 R_NATURAL_INC_2013 R_NATURAL_INC_2014 R_NATURAL_INC_2015 R_NATURAL_INC_2016 R_NATURAL_INC_2017 R_NATURAL_INC_2018 R_NATURAL_INC_2019 R_INTERNATIONAL_MIG_2011 R_INTERNATIONAL_MIG_2012 R_INTERNATIONAL_MIG_2013 R_INTERNATIONAL_MIG_2014 R_INTERNATIONAL_MIG_2015 R_INTERNATIONAL_MIG_2016 R_INTERNATIONAL_MIG_2017 R_INTERNATIONAL_MIG_2018 R_INTERNATIONAL_MIG_2019 R_DOMESTIC_MIG_2011 R_DOMESTIC_MIG_2012 R_DOMESTIC_MIG_2013 R_DOMESTIC_MIG_2014 R_DOMESTIC_MIG_2015 R_DOMESTIC_MIG_2016 R_DOMESTIC_MIG_2017 R_DOMESTIC_MIG_2018 R_DOMESTIC_MIG_2019 R_NET_MIG_2011 R_NET_MIG_2012 R_NET_MIG_2013 R_NET_MIG_2014 R_NET_MIG_2015 R_NET_MIG_2016 R_NET_MIG_2017 R_NET_MIG_2018 R_NET_MIG_2019
count 3152.000000 3.152000e+03 3152.000000 3152.000000 3152.000000 3111.000000 3111.000000 3111.000000 3111.000000 3111.000000 3111.000000 3111.000000 3111.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000 3112.000000
mean 30299.778236 5.026445e+04 0.647805 0.333851 0.823287 30.443748 34.293057 21.773192 13.489489 22.653970 34.698329 26.129572 16.516651 13.085088 34.147758 30.802965 21.964189 14.470662 19.998008 18.760347 11.707480 11.612219 11.618834 11.680572 11.729868 11.645475 11.437929 11.133515 10.976349 10.232143 10.153776 10.555303 10.434376 10.836281 10.803704 11.104026 10.426232 10.440449 1.475337 1.458443 1.063531 1.246196 0.893588 0.841771 0.333903 0.707283 0.535900 0.838683 0.953755 0.932902 1.020657 1.197376 1.210297 1.029114 0.734877 0.627073 -1.736386 -2.703292 -1.624192 -1.805175 -1.602156 -1.306236 -0.177991 0.070918 -0.255445 -0.897703 -1.749537 -0.691290 -0.784519 -0.404780 -0.095939 0.851123 0.805796 0.371628
std 15209.461517 1.493792e+05 0.162014 0.159852 0.381486 10.340168 6.147550 5.900026 6.575543 8.725873 6.559755 5.633778 7.800555 6.265950 7.226061 5.211923 9.582236 5.793896 8.493742 8.239852 2.655749 2.643972 2.630171 2.625713 2.604639 2.464632 2.505717 2.444717 2.355708 2.708478 2.712885 2.798850 2.734283 2.847367 2.831223 2.896709 2.694074 2.650735 4.324324 4.267707 4.282778 4.235978 4.291772 4.168065 4.225746 3.950688 3.904831 1.565648 1.948091 1.645713 1.872180 2.070665 2.187202 2.008643 1.583231 1.405652 10.244051 11.634220 11.863624 11.094490 12.432815 13.130463 15.138736 11.605549 12.420941 10.453551 11.934760 11.873776 11.173983 12.466564 13.110138 15.032059 11.567896 12.306269
min 1001.000000 6.600000e+01 0.053973 0.030909 0.000000 4.500000 13.500000 6.800000 3.700000 3.000000 10.900000 9.900000 4.900000 1.116910 7.265136 5.235602 0.000000 2.700000 2.400000 2.200000 2.574003 2.259887 1.445087 3.590664 3.646308 2.072539 0.000000 0.000000 0.795545 0.000000 0.794281 0.000000 0.953743 1.061008 1.525553 1.785449 0.000000 0.000000 -15.641513 -16.687737 -18.448924 -17.961734 -17.140359 -24.337867 -24.000000 -15.430175 -13.540491 -2.042901 -1.818182 -1.525165 -1.791083 -2.202037 -1.677852 -1.671775 -2.847077 -1.735609 -124.542124 -100.717079 -110.730202 -174.358974 -99.217169 -124.709564 -432.890317 -73.672141 -165.250965 -124.542124 -99.447514 -99.605727 -174.358974 -76.523881 -110.294466 -422.666861 -74.122609 -165.385261
25% 18162.500000 5.414750e+03 0.554128 0.209978 1.000000 22.800000 30.300000 17.300000 9.200000 16.100000 30.700000 22.100000 11.200000 8.479882 29.689806 27.290243 15.325250 10.400000 13.700000 12.600000 10.045831 9.949819 9.994716 10.085164 10.157151 10.118679 9.916906 9.690347 9.534427 8.454593 8.392970 8.734189 8.628567 8.989829 8.938050 9.179629 8.793976 8.804396 -1.266710 -1.162883 -1.613222 -1.497324 -1.848886 -1.814585 -2.362061 -1.808598 -1.980223 0.000000 0.000000 0.000000 0.000000 0.059195 0.071263 0.000000 0.000000 0.000000 -6.246028 -7.737542 -6.695351 -7.094020 -7.179978 -7.304745 -6.526173 -6.194214 -5.951671 -5.810970 -7.202896 -6.032234 -6.221956 -6.005191 -6.231558 -5.460539 -5.494566 -5.335441
50% 29166.000000 1.233550e+04 0.681720 0.300235 1.000000 28.600000 33.800000 21.700000 11.800000 20.800000 34.800000 26.200000 14.500000 11.739182 34.538523 30.785382 19.557242 13.400000 18.700000 17.500000 11.515327 11.434897 11.459053 11.513618 11.522429 11.478833 11.300128 11.043734 10.892555 10.208532 10.116319 10.592160 10.420924 10.855084 10.894936 11.155377 10.559963 10.514913 1.195682 1.195345 0.760905 0.915248 0.517815 0.480427 0.089535 0.423801 0.288647 0.327824 0.339041 0.389089 0.423523 0.515844 0.523741 0.408624 0.267661 0.214936 -1.842191 -2.749527 -1.954317 -2.043082 -2.050794 -1.707115 -0.436450 -0.503594 -0.717189 -1.033011 -1.970939 -1.130729 -1.153278 -1.007086 -0.673901 0.510675 0.301774 -0.087522
75% 45075.500000 3.330450e+04 0.773776 0.425830 1.000000 38.150000 38.500000 25.900000 15.600000 28.800000 38.950000 30.100000 19.300000 16.708521 39.099957 34.214836 25.989834 17.500000 24.925000 23.500000 13.005422 12.979334 12.940443 13.052812 13.049309 12.858924 12.733382 12.358365 12.149144 11.930570 11.868338 12.324561 12.237813 12.629175 12.662619 12.903265 12.243657 12.233626 3.917346 3.870601 3.478208 3.571822 3.285009 3.258075 2.758224 2.863530 2.698155 1.064359 1.119307 1.221140 1.283637 1.472330 1.431512 1.195190 0.888390 0.760167 2.631560 2.364453 3.185209 3.302156 3.797683 4.568741 6.790144 6.106826 5.446392 3.797153 3.369216 4.229097 4.313056 5.021001 5.954312 7.582870 6.702946 5.942094
max 56045.000000 4.263443e+06 0.961818 0.921497 1.000000 68.400000 52.600000 43.300000 53.400000 65.300000 53.200000 44.900000 63.700000 73.560211 57.433674 60.563381 77.557411 47.700000 63.400000 55.000000 29.658194 30.045068 29.339398 29.114243 30.412840 29.038718 28.490465 28.538363 28.192024 24.941873 22.572402 25.281859 24.836601 25.403314 34.359341 26.492118 25.561097 22.101890 24.214242 24.771735 26.014444 25.826296 27.065846 24.557893 24.992980 25.002861 24.429062 16.374269 42.683445 20.855416 25.142857 31.534570 43.489045 30.556449 26.668758 26.041667 122.905028 119.420909 208.333333 149.972849 278.846154 208.819282 152.000000 71.174377 126.182965 122.905028 119.154046 208.333333 149.874118 278.846154 208.597134 152.000000 71.174377 126.182965

2. Premières modélisations avec 2 variables¶

Dans cette partie, on ne conserve que 2 variables afin de réaliser des graphes en 2D¶

2.1 Choix des variables¶

In [30]:
# Création de la matrice de corrélation pour pouvoir sélectionner les 2 variables les plus influentes
mat_cor=data_complet.corr()
In [31]:
mat_cor['per_gop'].sort_values(ascending=False)
Out[31]:
per_gop                     1.000000
target                      0.781093
pc_high_school_00           0.497250
pc_high_school_15-19        0.488548
R_death_2012                0.394333
R_death_2013                0.384782
R_death_2014                0.383803
pc_high_school_90           0.378037
R_death_2015                0.377787
R_death_2011                0.377161
R_death_2017                0.366228
R_death_2016                0.360760
pc_no_diploma_90            0.271382
R_death_2018                0.253079
R_death_2019                0.226521
pc_no_diploma_00            0.194518
pc_degree_15-19             0.154604
pc_no_diploma_15-19         0.134040
county_fips                 0.076310
R_DOMESTIC_MIG_2019         0.062288
R_DOMESTIC_MIG_2015         0.049846
R_DOMESTIC_MIG_2018         0.049562
R_DOMESTIC_MIG_2013         0.047139
R_NET_MIG_2019              0.030047
R_DOMESTIC_MIG_2014         0.015267
R_DOMESTIC_MIG_2011         0.009212
R_NET_MIG_2018              0.008705
PCTPOV017_2019              0.000021
R_DOMESTIC_MIG_2012        -0.000005
R_DOMESTIC_MIG_2017        -0.005536
R_NET_MIG_2013             -0.006415
R_NET_MIG_2015             -0.011470
R_birth_2016               -0.013090
R_DOMESTIC_MIG_2016        -0.013310
PCTPOV517_2019             -0.015105
R_birth_2017               -0.018849
R_birth_2018               -0.022998
R_birth_2019               -0.028547
R_birth_2015               -0.038596
R_NET_MIG_2014             -0.046276
R_NET_MIG_2011             -0.047160
R_NET_MIG_2017             -0.049274
PCTPOVALL_2019             -0.054759
R_birth_2014               -0.055459
R_NET_MIG_2012             -0.056175
R_NET_MIG_2016             -0.068173
R_birth_2013               -0.070765
pc_degree_00               -0.103218
R_birth_2012               -0.127114
R_birth_2011               -0.138154
R_NATURAL_INC_2019         -0.170992
R_NATURAL_INC_2018         -0.186812
pc_degree_90               -0.230562
R_NATURAL_INC_2016         -0.252793
R_NATURAL_INC_2017         -0.262222
R_NATURAL_INC_2015         -0.274066
R_NATURAL_INC_2014         -0.282118
R_INTERNATIONAL_MIG_2019   -0.287344
R_NATURAL_INC_2013         -0.294919
R_INTERNATIONAL_MIG_2018   -0.299697
R_NATURAL_INC_2011         -0.321075
R_INTERNATIONAL_MIG_2017   -0.327024
R_INTERNATIONAL_MIG_2016   -0.328727
R_NATURAL_INC_2012         -0.329419
R_INTERNATIONAL_MIG_2012   -0.344120
R_INTERNATIONAL_MIG_2014   -0.366666
R_INTERNATIONAL_MIG_2015   -0.368348
total_votes                -0.372754
R_INTERNATIONAL_MIG_2011   -0.375152
R_INTERNATIONAL_MIG_2013   -0.386103
pc_high_degree_15-19       -0.540161
pc_high_degree_00          -0.561179
pc_high_degree_90          -0.573438
per_dem                    -0.994158
Name: per_gop, dtype: float64
In [32]:
# Visualisation avec 2 variables
#data_visu=data_complet[['pc_high_degree_90','R_death_2012','target']]
data_visu=data_complet[['pc_high_school_00','PCTPOV517_2019','target']]
#data_visu=data_complet[['pc_no_diploma_00','R_death_2012','target']]

sns.pairplot(data_visu, hue="target", height=5)
Out[32]:
<seaborn.axisgrid.PairGrid at 0x1ef906ab790>
In [106]:
#fig = px.scatter_3d(data_complet, x='pc_high_school_00', y='R_death_2012', z='PCTPOV517_2019',
#              color='target')
#fig.show()

2.2 Régression logistique à partir de 2 variables¶

https://medium.com/@cmukesh8688/sklearn-pipeline-gridsearchcv-54f5552bbf4e

In [161]:
#X=data_educ_pov[['pc_high_degree_15-19','PCTPOV517_2019']].values
#y=data_educ_pov[['target']].values.ravel()
In [34]:
X=data_complet[['pc_high_degree_15-19','PCTPOV517_2019']]
#X=data_complet[['pc_high_school_00','PCTPOV517_2019']]
y=data_complet['target']
In [35]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)
In [36]:
# pipeline complet
#preproc = make_pipeline(SimpleImputer(strategy="median"),StandardScaler())

log_reg = LogisticRegression()

preproc = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler())
    
])


pipe = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('log_reg', log_reg)
])

pipe.fit(X_train,y_train)
Out[36]:
Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
                ('scaler', StandardScaler()),
                ('log_reg', LogisticRegression())])
In [37]:
y_pred = pipe.predict(X_valid)
print("Bilan de la régression logistique de base:")
print(f"- La matrice de confusion est: \n{confusion_matrix(y_valid, y_pred)} ")
print("")
print(f"- Le score f1 vaut: {f1_score(y_valid, y_pred):.2f}.")
print(f"- Le recall vaut: {recall_score(y_valid, y_pred):.2f}.")
print(f"- La précision vaut: {precision_score(y_valid, y_pred):.2f}.")

Bilan de la régression logistique de base:
- La matrice de confusion est: 
[[ 53  66]
 [ 21 491]] 

- Le score f1 vaut: 0.92.
- Le recall vaut: 0.96.
- La précision vaut: 0.88.
In [38]:
coefs = pipe[-1].coef_[0]  # coefficients = 2d array
weights = pd.Series(coefs, index=X.columns.to_list())
weights.plot.barh()
_ = plt.title("Poids des variables de la régression logistique")
In [39]:
# pipeline sur le preprocessing pour X
# transformation y en vecteur
# pour affichage des frontières de décision


X_train_pr = preproc.fit_transform(X_train)


# Plotting decision regions
ax, fig = plt.subplots(figsize = (14,10))
plot_decision_regions(X_train_pr, y_train.values.ravel(), clf=log_reg, legend=2)

# Adding axes annotations

plt.xlabel('pc_high_degree_15-19')
plt.ylabel('PCTPOV517_2019')
plt.title('Régression logistique avec 2 variables ')
plt.show()

2.3 Régression logistique avec 2 variables:¶

Optimisation avec gridsearch¶

In [40]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)


# Utilisation de gridsearch CV avec une régression logistique


pipe = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('logreg', log_reg)
])

# Create new parameter dictionary
grid_params = {
    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values
    'logreg__solver' : ('saga', 'liblinear'),
    'logreg__penalty': ('l1', 'l2'),
    'logreg__C': (0.01, 0.1, 1, 10),
    'logreg__fit_intercept': (True, False)}



# Création de l'objet gridsearch object 
    
gs_reg = GridSearchCV(pipe, grid_params)

gs_reg.fit(X_train,y_train.values.ravel())
Out[40]:
GridSearchCV(estimator=Pipeline(steps=[('imputer',
                                        SimpleImputer(strategy='median')),
                                       ('scaler', StandardScaler()),
                                       ('logreg', LogisticRegression())]),
             param_grid={'logreg__C': (0.01, 0.1, 1, 10),
                         'logreg__fit_intercept': (True, False),
                         'logreg__penalty': ('l1', 'l2'),
                         'logreg__solver': ('saga', 'liblinear')})
In [41]:
# Sélection du meilleur modèle
best_reg = gs_reg.best_estimator_

# Impact de l'optimisation sur le score
y_pred_best_reg = best_reg.predict(X_valid)
print('Bilan de la régression logistique optimisée:')
print(f"- La matrice de confusion est: \n{confusion_matrix(y_valid, y_pred_best_reg)} ")
print("")
print(f"- Le score f1 vaut: {f1_score(y_valid, y_pred_best_reg):.2f}.")
print(f"- Le recall vaut: {recall_score(y_valid, y_pred_best_reg):.2f}.")
print(f"- La précision vaut: {precision_score(y_valid, y_pred_best_reg):.2f}.")

Bilan de la régression logistique optimisée:
- La matrice de confusion est: 
[[ 53  66]
 [ 22 490]] 

- Le score f1 vaut: 0.92.
- Le recall vaut: 0.96.
- La précision vaut: 0.88.
In [42]:
# Affichage des meilleurs paramètres
print(f"Les meilleurs paramètres sont: {gs_reg.best_params_}.")

Les meilleurs paramètres sont: {'logreg__C': 1, 'logreg__fit_intercept': True, 'logreg__penalty': 'l1', 'logreg__solver': 'saga'}.

Aucun impact de l'optimisation.

2.4 SVM non linéaire avec gridsearch¶

https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html

In [44]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)


# Utilisation de gridsearch CV avec une régression logistique


pipe = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('svc', SVC())
])
In [45]:
 
# Paramètres pour gridsearch

grid_params_svc = {
    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values
    'svc__C' : (0.1, 1, 10, 100, 1000),
    'svc__gamma': (1, 0.1, 0.01, 0.001),
    'svc__kernel': ['rbf','sigmoid']}


pipe_svc = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('svc', SVC())
])


# Création de l'objet gridsearch object  et fit    
gs_svc = GridSearchCV(pipe_svc, grid_params_svc, cv=5, refit=True, verbose=2)

gs_svc.fit(X_train,y_train.values.ravel())

Fitting 5 folds for each of 40 candidates, totalling 200 fits
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.4s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.3s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.6s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.4s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.4s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   3.0s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   2.1s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   3.9s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   2.0s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   2.9s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.5s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.4s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.7s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.5s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.4s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
Out[45]:
GridSearchCV(cv=5,
             estimator=Pipeline(steps=[('imputer',
                                        SimpleImputer(strategy='median')),
                                       ('scaler', StandardScaler()),
                                       ('svc', SVC())]),
             param_grid={'svc__C': (0.1, 1, 10, 100, 1000),
                         'svc__gamma': (1, 0.1, 0.01, 0.001),
                         'svc__kernel': ['rbf', 'sigmoid']},
             verbose=2)
In [46]:
# Choix du meilleur modèle
best_svm = gs_svc.best_estimator_

# Score de la svm optimisée
y_pred_best_svm = best_svm.predict(X_valid)
print('Bilan de la svm optimisée:')
print(f"- La matrice de confusion est: \n{confusion_matrix(y_valid, y_pred_best_svm)} ")
print("")
print(f"- Le score f1 vaut: {f1_score(y_valid, y_pred_best_svm):.2f}.")
print(f"- Le recall vaut: {recall_score(y_valid, y_pred_best_svm):.2f}.")
print(f"- La précision vaut: {precision_score(y_valid, y_pred_best_svm):.2f}.")

Bilan de la svm optimisée:
- La matrice de confusion est: 
[[ 48  71]
 [ 16 496]] 

- Le score f1 vaut: 0.92.
- Le recall vaut: 0.97.
- La précision vaut: 0.87.

Pas d'amélioration du score F1.

In [78]:
#scores = gs_svc.cv_results_['mean_test_score']
#scores.sort()
#scores
In [47]:
print('Voici les meilleurs paramètres pour la SVM:')
gs_svc.best_params_

Voici les meilleurs paramètres pour la SVM:
Out[47]:
{'svc__C': 1000, 'svc__gamma': 0.1, 'svc__kernel': 'rbf'}
In [48]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)

pipe_svc_best = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('svc', SVC(C=1000, gamma=0.1, kernel='rbf'))
])


preproc = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler())
    
])

svc = SVC(C=1000, gamma=0.1, kernel='rbf')


####################### 

X_train_pr = preproc.fit_transform(X_train)

svc.fit(X_train_pr,y_train.values.ravel())


#######################


#pipe_svc_best.fit(X_train,y_train.values.ravel())
Out[48]:
SVC(C=1000, gamma=0.1)
In [49]:
#######################

# Tracé des frontières de décision
ax, fig = plt.subplots(figsize = (14,10))
plot_decision_regions(X_train_pr, y_train.values.ravel(), clf=svc, legend=2)

# Titres

plt.xlabel('pc_high_degree_15-19')
plt.ylabel('PCTPOV517_2019')
plt.title('SVC ')
plt.show()
  • https://zekehochberg.github.io/pipeline-and-gridsearch/
  • https://medium.com/@cmukesh8688/sklearn-pipeline-gridsearchcv-54f5552bbf4e

3. Random forest:¶

Il s'agit de déterminer les variables les plus influentes pour effectuer une analyse plus poussée.¶

-https://www.kaggle.com/code/pepacz/randomforestclassifier-with-sklearn-pipeline

3.1. Choix des variables: préselection initiale¶

In [50]:
# Création X et y
X=data_complet.select_dtypes(exclude=['object'])
X.drop(columns=['county_fips','total_votes','per_gop','per_dem','target'], inplace=True)
y=data_complet['target']
In [51]:
list_col=X.columns.tolist()
In [52]:
X.shape
Out[52]:
(3152, 69)

3.2. Modèle de base¶

In [69]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)


# Utilisation de gridsearch CV avec une random forest

# warm_start=True pour avoir accès à estimator_

rf_clf = RandomForestClassifier(n_estimators=200, n_jobs=10, 
                                random_state=42,
                                max_depth=5,
                               warm_start=True)

preproc = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler())
    
])

pipe_rf = Pipeline([
    ('preprocessor', preproc), 
    ('classifier', rf_clf)
])

pipe_rf.fit(X_train, y_train)
Out[69]:
Pipeline(steps=[('preprocessor',
                 Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
                                 ('scaler', StandardScaler())])),
                ('classifier',
                 RandomForestClassifier(max_depth=5, n_estimators=200,
                                        n_jobs=10, random_state=42,
                                        warm_start=True))])
In [70]:
# Scores du modèle Random Forest de base
y_pred_rnd_for = pipe_rf.predict(X_valid)
print("Bilan d'une forêt aléatoire simple:")
print(f"- La matrice de confusion est: \n{confusion_matrix(y_valid, y_pred_rnd_for)} ")
print("")
print(f"- Le score f1 vaut: {f1_score(y_valid, y_pred_rnd_for):.2f}.")
print(f"- Le recall vaut: {recall_score(y_valid, y_pred_rnd_for):.2f}.")
print(f"- La précision vaut: {precision_score(y_valid, y_pred_rnd_for):.2f}.")

Bilan d'une forêt aléatoire simple:
- La matrice de confusion est: 
[[ 57  62]
 [ 13 499]] 

- Le score f1 vaut: 0.93.
- Le recall vaut: 0.97.
- La précision vaut: 0.89.

3.3. Optimisation du degré de randomization¶

In [65]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)
thresholds = np.linspace(0.01, 1, num=10)
scores = []

# Boucle sur thresholds avec sauvegarde des scores

for t in thresholds:
    rf_clf = RandomForestClassifier(n_estimators=200, 
                                    max_features=t,
                                    n_jobs=10, 
                                    random_state=42,
                                   warm_start=True)

    pipe_rf = Pipeline([
        ('preprocessor', preproc), 
        ('classifier', rf_clf)
    ])

    y_train_pred = cross_val_predict(pipe_rf, X_train, y_train, 
                                     cv=5, n_jobs=5)

    scores.append(f1_score(y_train, y_train_pred))
In [66]:
# Tracé des scores en fonctions du seuil
plt.plot(thresholds, scores, marker='*')
_=plt.xlabel("% de variables à chaque split")
_=plt.ylabel("Score F1")

print(f"Valeur optimale de max_features : {thresholds[np.argmax(scores)]}")
print(f"Score F1 : {np.max(scores)}")

Valeur optimale de max_features : 0.56
Score F1 : 0.9372952737482453

3.4.Visualisation des arbres de la forêt aléatoire¶

  • https://towardsdatascience.com/how-to-visualize-a-decision-tree-from-a-random-forest-in-python-using-scikit-learn-38ad2d75f21c
  • https://mljar.com/blog/visualize-tree-from-random-forest/
In [71]:
# Enregistrement du classifieur de la pipeline pipe_rf sous model
model=pipe_rf.steps[1][1]
model
Out[71]:
RandomForestClassifier(max_depth=5, n_estimators=200, n_jobs=10,
                       random_state=42, warm_start=True)
In [74]:
estimator = model.estimators_
print(f"Le nombre d'arbres de la forêt aléatoire est de {len(estimator)}.")

Le nombre d'arbres de la forêt aléatoire est de 200.
In [75]:
# Visualisation de l'arbre n
feature_names=list(X_train.columns)
n=0
plt.figure(figsize=(20,20))
_ = tree.plot_tree(model.estimators_[n], filled=True, fontsize=6)

# Sauvegarde avec une haute résolution
plt.savefig("decistion_tree.jpeg", dpi=200)
In [76]:
# Taille de l'arbre
model.estimators_[0].tree_.max_depth
Out[76]:
5

3.5. Importance des variables¶

In [77]:
_ = pipe_rf.fit(X_train, y_train)

C:\Users\sylvi\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py:429: UserWarning:

Warm-start fitting without increasing n_estimators does not fit new trees.
In [78]:
col_names = X_train.columns
features_importances = pipe_rf.named_steps["classifier"].feature_importances_
forest_importances = pd.Series(features_importances, index=col_names)
In [80]:
# Choix des 20 premières variables
forest_importances.sort_values(ascending=False, inplace=True)
forest_importances=forest_importances.iloc[:20]
In [275]:
# Tracé de l'influence des variables
fig, ax = plt.subplots(figsize=(20,10))
forest_importances.plot.bar(ax=ax)
ax.set_title("Importance des 20 premières variables")
ax.set_ylabel("Baisse moyenne de l'impureté")
_=plt.xticks(rotation = 70)
#fig.tight_layout()
In [86]:
# Sélection fine des variables
fine_selection = forest_importances.index[0:10]
In [87]:
# Mise à jour de X à partir des résultats de random forest
# Création X et y
X=data_complet.select_dtypes(exclude=['object'])
X.drop(columns=['county_fips','total_votes','per_gop','per_dem','target'], inplace=True)
y=data_complet['target']
X=X[fine_selection]
In [88]:
# Visualisation des corrélations
f, ax = plt.subplots(figsize=[20,20])
sns.heatmap(pd.concat([X, y], axis=1).corr(),
            annot=True, fmt=".2f",cbar_kws={'label': 'Percentage %'},cmap="plasma",ax=ax)
ax.set_title("Matrice de corrélations")
plt.show()

3.6.SVM après sélection des variables influentes¶

https://towardsdatascience.com/how-to-visualize-a-decision-tree-from-a-random-forest-in-python-using-scikit-learn-38ad2d75f21c

In [101]:
# Création X et y
X=data_complet.select_dtypes(exclude=['object'])
X.drop(columns=['county_fips','total_votes','per_gop','per_dem','target'], inplace=True)
y=data_complet['target']
X=X[fine_selection]
In [104]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)
In [105]:
 
# Paramètres pour gridsearch

grid_params_svc = {
    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values
    'svc__C' : (0.1, 1, 10, 100, 1000),
    'svc__gamma': (1, 0.1, 0.01, 0.001),
    'svc__kernel': ['rbf','sigmoid']}


pipe_svc = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('svc', SVC())
])


# Création de l'objet gridsearch object  et fit    
gs_svc = GridSearchCV(pipe_svc, grid_params_svc, cv=5, refit=True, verbose=2)

gs_svc.fit(X_train,y_train.values.ravel())

Fitting 5 folds for each of 40 candidates, totalling 200 fits
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ..........svc__C=0.1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=0.1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=0.1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=0.1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=0.1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=0.1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=0.1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ............svc__C=1, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=1, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=1, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=1, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ...........svc__C=10, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=10, svc__gamma=0.1, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=10, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ........svc__C=10, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=10, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=10, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=10, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END ..........svc__C=100, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ........svc__C=100, svc__gamma=0.1, svc__kernel=rbf; total time=   0.1s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ....svc__C=100, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END .......svc__C=100, svc__gamma=0.01, svc__kernel=rbf; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=100, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ......svc__C=100, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=100, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   0.0s
[CV] END .........svc__C=1000, svc__gamma=1, svc__kernel=rbf; total time=   0.1s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=1, svc__kernel=sigmoid; total time=   0.0s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.4s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.4s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.4s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.4s
[CV] END .......svc__C=1000, svc__gamma=0.1, svc__kernel=rbf; total time=   0.4s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ...svc__C=1000, svc__gamma=0.1, svc__kernel=sigmoid; total time=   0.0s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.2s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.2s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.2s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.2s
[CV] END ......svc__C=1000, svc__gamma=0.01, svc__kernel=rbf; total time=   0.1s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END ..svc__C=1000, svc__gamma=0.01, svc__kernel=sigmoid; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .....svc__C=1000, svc__gamma=0.001, svc__kernel=rbf; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
[CV] END .svc__C=1000, svc__gamma=0.001, svc__kernel=sigmoid; total time=   0.0s
Out[105]:
GridSearchCV(cv=5,
             estimator=Pipeline(steps=[('imputer',
                                        SimpleImputer(strategy='median')),
                                       ('scaler', StandardScaler()),
                                       ('svc', SVC())]),
             param_grid={'svc__C': (0.1, 1, 10, 100, 1000),
                         'svc__gamma': (1, 0.1, 0.01, 0.001),
                         'svc__kernel': ['rbf', 'sigmoid']},
             verbose=2)
In [106]:
# Choix du meilleur modèle
best_svm = gs_svc.best_estimator_

# Score de la svm optimisée avec les meilleurs variables (d'après la random forest utilisée)
y_pred_best_svm = best_svm.predict(X_valid)
print('Bilan de la svm optimisée:')
print(f"- La matrice de confusion est: \n{confusion_matrix(y_valid, y_pred_best_svm)} ")
print("")
print(f"- Le score f1 vaut: {f1_score(y_valid, y_pred_best_svm):.2f}.")
print(f"- Le recall vaut: {recall_score(y_valid, y_pred_best_svm):.2f}.")
print(f"- La précision vaut: {precision_score(y_valid, y_pred_best_svm):.2f}.")

Bilan de la svm optimisée:
- La matrice de confusion est: 
[[ 62  57]
 [ 30 482]] 

- Le score f1 vaut: 0.92.
- Le recall vaut: 0.94.
- La précision vaut: 0.89.

4.0 XGBoost¶

In [107]:
# Création X et y
X=data_complet.select_dtypes(exclude=['object'])
X.drop(columns=['county_fips','total_votes','per_gop','per_dem','target'], inplace=True)
y=data_complet['target']
X=X[fine_selection]
In [108]:
# Séparation entre train et valid
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)
In [115]:
# Création d'une pipeline

preproc = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler())
    
])

model=xgb.XGBClassifier()

pipe_xgb = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('model', model )])

# Définition de la grille de parametres

param_list = {"model__max_depth": (3,5,8,10,15,20),
              "model__min_child_weight" : (1,2,3,4,5),
              "model__n_estimators": (20,50,100,200),
               "model__learning_rate": [0.0001,0.001,0.005,0.01,0.015,0.02],}
# grid search

gs_xgb = GridSearchCV(pipe_xgb, param_list, cv=5, refit=True, verbose=2)


gs_xgb.fit(X_train,y_train.values.ravel())

Fitting 5 folds for each of 720 candidates, totalling 3600 fits
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.6s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.0001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.001, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.005, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.6s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.01, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.015, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=1, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=2, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=3, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=4, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=100; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=3, model__min_child_weight=5, model__n_estimators=200; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=1, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=2, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=3, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=4, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=5, model__min_child_weight=5, model__n_estimators=200; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=2, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=8, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=1, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=10, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=15, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=1, model__n_estimators=200; total time=   0.5s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=2, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=100; total time=   0.2s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=3, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=4, model__n_estimators=200; total time=   0.4s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=20; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=50; total time=   0.0s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=100; total time=   0.1s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
[CV] END model__learning_rate=0.02, model__max_depth=20, model__min_child_weight=5, model__n_estimators=200; total time=   0.3s
Out[115]:
GridSearchCV(cv=5,
             estimator=Pipeline(steps=[('imputer',
                                        SimpleImputer(strategy='median')),
                                       ('scaler', StandardScaler()),
                                       ('model',
                                        XGBClassifier(base_score=None,
                                                      booster=None,
                                                      callbacks=None,
                                                      colsample_bylevel=None,
                                                      colsample_bynode=None,
                                                      colsample_bytree=None,
                                                      early_stopping_rounds=None,
                                                      enable_categorical=False,
                                                      eval_metric=None,
                                                      feature_types=None,
                                                      gamma=None,...
                                                      max_leaves=None,
                                                      min_child_weight=None,
                                                      missing=nan,
                                                      monotone_constraints=None,
                                                      n_estimators=100,
                                                      n_jobs=None,
                                                      num_parallel_tree=None,
                                                      predictor=None,
                                                      random_state=None, ...))]),
             param_grid={'model__learning_rate': [0.0001, 0.001, 0.005, 0.01,
                                                  0.015, 0.02],
                         'model__max_depth': (3, 5, 8, 10, 15, 20),
                         'model__min_child_weight': (1, 2, 3, 4, 5),
                         'model__n_estimators': (20, 50, 100, 200)},
             verbose=2)
In [271]:
#sk.metrics.get_scorer_names()
In [116]:
print(f"Les meilleurs paramètres de XGBoost sont: {gs_xgb.best_params_}. ")

Les meilleurs paramètres de XGBoost sont: {'model__learning_rate': 0.015, 'model__max_depth': 5, 'model__min_child_weight': 2, 'model__n_estimators': 20}.
In [117]:
# Choix du meilleur modèle
best_xgb=gs_xgb.best_estimator_

# Scores du meilleur modèle XGBoost
y_pred_best_svm = best_xgb.predict(X_valid)
print('Bilan avec XGBOOST:')
print(f"- La matrice de confusion est: \n{confusion_matrix(y_valid, y_pred_best_svm)} ")
print("")
print(f"- Le score f1 vaut: {f1_score(y_valid, y_pred_best_svm):.2f}.")
print(f"- Le recall vaut: {recall_score(y_valid, y_pred_best_svm):.2f}.")
print(f"- La précision vaut: {precision_score(y_valid, y_pred_best_svm):.2f}.")

Bilan avec XGBOOST:
- La matrice de confusion est: 
[[ 63  56]
 [ 26 486]] 

- Le score f1 vaut: 0.92.
- Le recall vaut: 0.95.
- La précision vaut: 0.90.

5. Stacking de modèles¶

In [125]:
# Création X et y
X=data_complet.select_dtypes(exclude=['object'])
X.drop(columns=['county_fips','total_votes','per_gop','per_dem','target'], inplace=True)
y=data_complet['target']
X=X[fine_selection]
In [141]:
# Définition des modèles à utiliser

rf = RandomForestClassifier(n_estimators=200,
                                    n_jobs=10, 
                                    random_state=42,
                                   warm_start=True)


pipe_svm = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('model_svm', best_svm )])

pipe_rf = Pipeline([
    ('imputer', SimpleImputer(strategy="median")), 
    ('scaler', StandardScaler()), 
    ('model_rf', rf )])


estimators = [
    ("rf", pipe_rf),
    ("svr", pipe_svm),
]

clf_stacked = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())

# Fit du modèle stacké
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, random_state=42)
clf_stacked.fit(X_train, y_train)
Out[141]:
StackingClassifier(estimators=[('rf',
                                Pipeline(steps=[('imputer',
                                                 SimpleImputer(strategy='median')),
                                                ('scaler', StandardScaler()),
                                                ('model_rf',
                                                 RandomForestClassifier(n_estimators=200,
                                                                        n_jobs=10,
                                                                        random_state=42,
                                                                        warm_start=True))])),
                               ('svr',
                                Pipeline(steps=[('imputer',
                                                 SimpleImputer(strategy='median')),
                                                ('scaler', StandardScaler()),
                                                ('model_svm',
                                                 Pipeline(steps=[('imputer',
                                                                  SimpleImputer(strategy='median')),
                                                                 ('scaler',
                                                                  StandardScaler()),
                                                                 ('svc',
                                                                  SVC(C=100,
                                                                      gamma=0.1))]))]))],
                   final_estimator=LogisticRegression())
In [142]:
# Scores du meilleur modèle de stacking
y_pred_stack = clf_stacked.predict(X_valid)
In [145]:
# Scores du meilleur modèle XGBoost
y_pred_stack =clf_stacked.predict(X_valid)
print('Bilan avec XGBOOST:')
print(f"- La matrice de confusion est: \n{confusion_matrix(y_valid, y_pred_best_svm)} ")
print("")
print(f"- Le score f1 vaut: {f1_score(y_valid, y_pred_stack):.2f}.")
print(f"- Le recall vaut: {recall_score(y_valid, y_pred_stack):.2f}.")
print(f"- La précision vaut: {precision_score(y_valid, y_pred_stack):.2f}.")

Bilan avec XGBOOST:
- La matrice de confusion est: 
[[ 63  56]
 [ 26 486]] 

- Le score f1 vaut: 0.93.
- Le recall vaut: 0.96.
- La précision vaut: 0.90.